Oklahoma City Travel Analysis

Nur Shlapobersky + Sage Voorhees
SES 5394: Travel Behavior and Forecasting Spring Semester 2023

Oklahoma City at a Glance

Figure 1: Sketch of Oklahoma City boundaries and interstate highways.

Sketch of Oklahoma City boundaries and interstate highways.

Oklahoma City is the capital of Oklahoma and the largest city in the state. Three major interstates–I-35, I-40, and I-44 all pass through OKC. As of the 2020 Census, the OKC Metro area is majority white, with a population just shy of 1.5 million people.1 2020 Census and 2021 American Community Survey

Race Percent of Population
White 62%
African American 10%
Native American 3%
Asian 3%
Multi-racial 8%
Other 1%
Hispanic 14%

Some well-known neighborhoods in OKC include

The Bricktown neighborhood. Figure 2: The Bricktown neighborhood.

Elements of the Model

Four Step Model

For this analysis we are using a classic four-step travel demand model. Because this is a student assignment with limited capacity, we are using various shortcuts throughout the model that will be identified. Shortcomings with the way we have modeled transportation do not necessarily reflect shortcomings of the four-step model.

Overview of the four-step model

Figure 3: Overview of the four-step model

Number of households by census tract Figure 4: Number of households by census tract

Transit Analysis Zones

Our transportation analysis looks at 419 transit analysis zones across 7 counties, each corresponding to a census tract. In Figure 4 we can see that Oklahoma City follows a typical greater metropolitan area pattern with a dense and active urban core, surrounded by suburbs and rural areas. The

Road Network

The longest distance between zones by car was just over 3 hours and 15 minutes (190.5 minutes). The shortest distance was half a minute (0.5 minutes). The average distance between TAZ centroids is roughly 30 minutes (30.7), the median time is around 25 minutes (25.6 minutes). Roads highlighted in red in Figure 5 were modeled as two-way rural roads.

Figure 5: The modeled Oklahoma City road network.


The modeled Oklahoma City road network.

Public Transit Network

Full county map with public transit. Figure 6: Full county map with public transit.

The OKC Transit network is composed of 651 miles of bus routes, across 30 different bus lines. The map below shows the bus network in detail, and in the context of the whole city. Of our 419 transit analysis zones for OKC metro area, the transit network connects only 135 of those zones, with the longest travel time between zones being just over 3 hours and 15 minutes (190.5 minutes). The shortest distance was half a minute (0.5 minutes). The average distance between centroids is roughly 30 minutes (30.7). The median time between centroids was around 25 minutes (25.6 minutes). The public transit is fully contained in 3 of the 7 counties that make up the OKC statistical area.

Figure 7: The public transit network in Oklahoma City.

The public transit network in Oklahoma City.

Travel Times

Using the networks we created, we generated travel time skims which provide travel times between every TAZ. By selecting a subset we can map every zone’s travel time by car to the University of Oklahoma, as in Figure 5.

Figure 8: Travel time by car from the University of Oklahoma

Travel time by car from the University of Oklahoma

As well as the travel time by bus from the University to other zones, as in Figure 6 (note that many are grayed out because they cannot be reached by bus).

Figure 9: Public transit travel time to the University of Oklahoma

Public transit travel time to the University of Oklahoma

Accessibility

Accessibility is a measure of how many destination travelers can reach within a perceived reasonable time using transportation modes available to them. Put an alternative, and slightly more mathematical way:

Mobility: reasonable reachable area Proximity: opportunities per area \[accessibility = mobility * proximity\]

We determine accessibility based on the network skims mentioned earlier and employment data. Travel times are used in a decay function to scale the “worth” of each opportunity, and these are all summed together to determine the accessibility score. See Appendix B for more information.

Accessibility by Car

Car access is distributed as is typical for a metropolitan area: the downtown, being both dense and centrally located, has higher scores than the outlying areas.

Figure 10: Car accessibility scores for each zone

Car accessibility scores for each zone

While they don’t necessarily represent a large percent of the land area, there are many of those downtown high-scoring zones because they are smaller, and this is what forms the right peak in the distribution shown in Figure 11. The left peak represents the outlying rural zones.

Figure 11: Distribution of zone accessibility scores

Distribution of zone accessibility scores

Accessibility by Transit

Transit in Oklahoma City is quite limited to the areas in and around Downtown and the University campus. The bus lines between the two areas notably bypass most of the zones in between, creating the two island-like regions in Figure 12. Taking a look at the linear scale accessibility map, we can see that the majority of those zones have very similar low scores. There are just a few outliers with much higher accessibility scores due to the proximity of transit hubs where many of the bus lines meet.

Figure 12: Transit accessibility scores for each zone (on a log scale and a linear scale)

Transit accessibility scores for each zone (on a log scale and a linear scale)

Those outliers can also be seen in Figure 13 at the far right tail of the distribution.

Figure 13: Distribution of zone accessibility scores

Distribution of zone accessibility scores

Trip Attractions and Trip Productions

Estimating Productions and Attractions

To generate Trip Attractions and Trip Productions for each transit analysis zones, we broke up trip types into three main categories.

  1. Home Based Work: Travel between work and home
  2. Home Based Other: Travel between home and places other than a workplace
  3. Non-Home Based: Travel that does not start or end at home

To generate our trip attractions and productions we conducted a linear regression using factors present in both the NHTS2 National Household Travel Survey (NHTS) from 2017 and the ACS.

We used:

  1. Median Income (Continuous)
  2. Whether or not the household had a vehicle (yes, no)
  3. Household size (1 person, 2 people, 3 people, 4 or more people)
  4. Whether or not the household had kids (yes, no)

We had a very low R-Squared value in our regressions ranging from .124 to .129. In our regressions, only household size and presence of kids was statistically significant. The resulting trips by type were as follows:

Trip Type Total Trips
Home Based Work 922,115
Home Based Other 2,829,372
Non Home Based 2,690,824

Figure 14: Home Based Other, Trip Productions and Attractions

Home Based Other, Trip Productions and Attractions
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Figure 15: Home Based Other in Scenario, Trip Productions and Attractions

Home Based Other in Scenario, Trip Productions and Attractions

Figure 16: Non-Home Based, Trip Productions and Attractions

Non-Home Based, Trip Productions and Attractions

We also used NHTS data to examine mode share in OKC based on various trip types.

Figure 17: Mode share in the OKC metro area

Mode share in the OKC metro area

Figure 18: Trip purpose by mode share in Oklahoma City metro area.

Trip purpose by mode share in Oklahoma City metro area.

Figure 19: Detailed trip purpose and mode share in Oklahoma City Metro Area

Detailed trip purpose and mode share in Oklahoma City Metro Area

Gravity Model

Calibrating the Model with Friction Functions

To build our gravity model and calculate our trip attraction/production matrix, we first had to choose friction functions for all three trip types. We chose to use power functions, with coefficients adjusted to cause our model’s average travel times to match the NHTS travel data.

For home-based work trips, \(friction = travelTime^{-3}\)

For home-based other trips, \(friction = travelTime^{-0.5}\)

For non-home-based trips, \(friction = travelTime^{-2.9}\)

This produced average travel times that closely matched observed data, as shown below.

Trip Type Observed Average Model Average
Home-Based Work 26.1019 26.1546
Home-Based Other 16.3041 16.3455
Non-Home-Based 15.9448 15.8869

Illustrating Travel Flows

We can see that the single county which generates the most trips by far (across all three types) originate in Cleveland county. This county includes the University of Oklahoma and a large amount of residential land. The vast majority of those trips end in Oklahoma County, which contains the Downtown area. Both the desire line plot and Chord diagrams below illustrate this.

Desire lines plotted between counties in Oklahoma City.

Figure 20: Desire lines plotted between counties in Oklahoma City.

Figure 21: Chord diagram for home-based work trips.

Figure 22: Chord diagram for home-based other trips.

Figure 23: Chord diagram for non-home-based trips.

Mode Choice

Generating Travel Costs

For this step of the model we began by generating costs for our three forms of transportation: driving alone (SOV), driving with someone else (HOV), and taking transit. Although our transit skim contained fare information, we chose to instead use information from the National Transit Database as we thought it would better reflect information about transit discounts. We then made the assumption that transit cost is a function of the baseline fare cost multiplied by the number of transfers. For travel by car we used NHTS data to first find total expenditure on gas and total driving time. Using these two numbers we generated cost per minute of driving. We then used information from table 4.16 of NCHRP 716 to estimate average occupancy of vehicles for our different categories of trip types (Home Based Work, Non-Home Based, Home-Based Other). We assume that driving cost is shared equally among all car occupants.

Transportation Mode Transportation Cost
Transit Base Fare $ 0.68
SOV $0.07 per mile
HOV (HBO) $0.025 per mile
HOV(NHB) $0.025 per mile
HOV (HBW) $0.025 per mile

Estimating Mode Shares

We then went into NHTS data to pull survey data about the existing mode share in OKC. We divided the modes into our three categories: HOV, SOV, and Transit.

We made the following assumptions:

Category Subcategories
Transit Public Bus, Para-transit, Private Bus, Rail
SOV Car, SUV/VAN, Pickup Truck, Rental Car, Rideshare/Taxi (all with occupancy = 1)
HOV Same as SOV (with occupancy > 1)
Not-Included Golf-Cart, Boat, Airplane, Snowmobile, ATV, Walking, Biking

We then used the NHTS survey weights to determine the percent of each of these three modes over our study area.

Choosing Models

In our process of choosing mode choice models from NCHRP 716 we went with two criteria 1. Using a Nested Logit when possible 2. Using models that we had all values for

We chose the following models:

Trip Type Model Chosen Assumptions of Model
Home Based Work Model G Nested; > 1 million; Excludes non-motorized; Submodes for HOV/SOV
Home Based Other Model G Non-nested; > 1 million; Excludes non-motorized; No submodes
Non Home Based Model G Non-nested; > 1 million; Excludes non-motorized; Submodes for HOV/SOV

Calculating Mode Share by Applying a Mode-Choice Model

Utility

Next we calculated utility for the different modes. This allows us to encode how much someone’s utility of a trip depends on characteristics of that trip. For example, how long someone has to wait for a bus decreases their perceived utility of transit, and how long someone has to drive decreases their utility of driving.The NCHRP models give us coefficients (how different aspects such as waiting time influence utility) but they don’t provide mode-specific constants which would tell us how the utilities of modes relate to each other. To estimate the utility of each mode we started by using the log-odds to generate total mode share for the region.

Probability and Ridership by Mode

We then used the calculated utilities in a probability model which generated the mode-share distribution for the region. By using the flow data generated by the gravity model described in the previous section, we were able to calculate the total ridership of our three different modes. Once we generated an initial estimate we were able to adjust our mode share coefficients to match the observed NHTS data, as shown in the table below:

HBO HBO Model HBW HBW Model NHB NHB Model
pct_SOV 0.430 0.431 0.905 0.903 0.510 0.514
pct_HOV 0.5416 0.5403 0.0716 0.0749 0.4771 0.4715
pct_transit 0.0286 0.0282 0.0231 0.0213 0.0125 0.0147

Trip Assignment

To begin the Trip Assignment step we first did some manipulations on our production-attraction matrix to convert the information from person based (how many people traveling between zones) to a vehicle based (how many cars traveling between zones). To do this, we divided the number of HOV trips by the predicted average carpool size for each type of trip. We used the same averages that we used in the Mode Choice step which came from table 4.16 of NCHRP 716. We then summed the three separate production-attraction matrices for all three trip types into one consolidated matrix that contained information for all types of trips (Home Based Work, Non Home Based and Home Based Other).

From this stage, we loaded our production-attraction matrix into TransCAD to convert the production-attraction matrix into a one-hour origin-destination matrix for the hour 5pm-6pm. We chose this time assuming that it would reveal information about peak congestion times.

The other things we needed for this step were information about the capacity of each link (road) in our network and free flow travel times. We calculated capacity by assuming that capacity is a function of the number of lanes and the speed of the road. We assumed that all roads that did not have lanes in the open street map dataset were 2 lane roads. We also assumed that the capacity for a given lane of road at 60 miles per hour is 1800 cars. To generate the capacity for each link we used the formula:

\[capacity = 1800 * number Of Lanes * (speed/60)\]

Once we had these three inputs, we were able to calculate vehicle volumes for each link in the network.

Figure 24: Road Congestion at 5pm in Oklahoma City. Orange and Red links show where the number of vehicles (v) exceeds the capacity (c) of the road

Road Congestion at 5pm in Oklahoma City. Orange and Red links show where the number of vehicles (v) exceeds the capacity (c) of the road

Scenario: Land Back Leads to a New Urban Area

Brief history of Oklahoma and Land Runs

Senakw precedent of dense urban development

New people coming in

Setting up the Scenario

Area of Interest

This scenario is concerned with the area of Oklahoma City contained within the Land Run of 1895. We geolocated the map in Figure 25 to identify which census tracts would become part of the new urban center. In total, six tracts with a total area of 353 square miles would be included.

A map of the Land Run of 1895 Figure 25: A map of the Land Run of 1895

Figure 26: The census tracts that would be returned to Kickapoo nation sovereignty

The census tracts that would be returned to Kickapoo nation sovereignty

Changing the Demographics

In order to model travel behavior under the new scenario, we needed to modify the demographics of the new urban area. We chose to reference the demographic make-up of a downtown census tract3 Census Tract 1019, Oklahoma County for seeding the new area, using the same ratios of household size, composition, and car ownership, while keeping the median income the same as what those tracts already were. We also kept the same ratio of population density to employment and activity density, while scaling the population to reach our desired total of ~250,000 new residents.

Evaluation Criteria

Travel flows VMT Congestion

Setup Limitations

If the population within this area had actually increased so drastically then census tract boundaries would have been redrawn, and new tracts would have been added. For our model, this would have meant many more TAZs and more accurate centroids of that new population. It was beyond the scope of this study to try and redraw census tract boundaries, so all the new population is unrealistically concentrated in the centers of the large census tracts.

Results

Flows

Desire lines plotted between counties in Oklahoma City.

Figure 27: Desire lines plotted between counties in Oklahoma City.

Increases in travel flow between counties in Oklahoma City.

Figure 28: Increases in travel flow between counties in Oklahoma City.

Decreases in travel flow between counties in Oklahoma City

Figure 29: Decreases in travel flow between counties in Oklahoma City

Figure 30: Chord diagram for home-based work trips.

Figure 31: Chord diagram for home-based other trips.

Figure 32: Chord diagram for non-home-based trips.

Vehicle Miles Traveled and Congestion

Old VMT: 22006966

New VMT: 40856275

Distribution of congestion shows more for our land back scenario

Figure 33: Histogram of road segment congestion in new scenario vs current conditions

Histogram of road segment congestion in new scenario vs current conditions

Analysis Limitations

Appendix

A: Methodology and Sources

Demographics and Land Use Data

For data about the population density, income, household size, and vehicle availability of we used 5-year Sample American Community Survey (ACS) Data from 2021. For information about the land use and employment we used Longitudinal Employer Household Dynamics (LEHD). For geographic boundaries we used census data.

Road Network

To generate the Road Network we used data pulled from Open Street Map, downloaded through the service . We included in our road network all road segments labeled as motorways, motorway_links, secondary, tertiary, trunks or unclassified roads. We decided to include the unclassified roads when we realized that major roads including US-77, US-62 were not included in motorways. Adding in unclassified roads also brought back in “boulevards,” such as Oklahoma City Boulevard and North Lincoln Boulevard. Our assumption is that since the original data did not label any roads as “primary,” many roads that would have been considered primary were instead labeled as unclassified.We then began to generate a transit skim using the software Transcad.

Public Transportation Network

In this model we used General Transit Feed Specification (GTFS) data pulled from Oklahoma City EMBARK’s GTFS feed.

B: Assumptions

Road Network

All primary and secondary roads in rural areas are two-way roads even if coded as one-way roads in the OSM data. This assumption was based on cross-referencing against satellite images that indicated roads had bi-directional traffic despite being coded as one-ways in OSM. We identified rural areas by looking at the network and selecting areas that had large, mostly rectangular Transit Area Zones (TAZs). See Figure 3 for an image of primary or secondary road segments that we treated as rural two-ways.

We made the following speed assumptions: * Unclassified road speeds are 30 mph * Motorways are 60 mph * Primary are 60 mph * Secondary are 40 mph * Tertiary are 30 mph * Centroid Connectors are 15.

In our model we assumed that centroid connectors could model residential roads in each TAZ. Centroid connectors can be up to 25 miles long, but must connect to a road no more than .1 miles outside of the zone boundary. Each centroid can have up to 7 centroid connectors.

Transit Network

  1. The maximum initial wait time for a public transit trip was 15 minutes.
  2. The walk speed for a traveler is 2.8 miles per hour.
  3. Buses move at 30 miles per hour.
  4. Centroid connectors could be a maximum of 0.5 miles long.

Accessibility Metrics

We are weighting the portion of time spent waiting for a bus or train as 2.5 times the in-vehicle travel time (IVTT)

We are using a logistic decay function with an inflection point of 25 and standard deviation of 5

C: Supplemental Visualizations

Census and Employment Data

Figure 34: Employment is concentrated in the downtown area. Employment information is not available for many of our Transit Analysis Zones

Employment is concentrated in the downtown area. Employment information is not available for many of our Transit Analysis Zones

Figure 35: Majority of employment in OKC metro area is in the service industry.

Majority of employment in OKC metro area is in the service industry.
Fig A2: Majority of employment in OKC metro area is in the service industry
Fig A2: Majority of employment in OKC metro area is in the service industry

Activity Density

Fig A3: Employment + Activity Density is greatest in downtown OKC

Fig A4: The majoirty of census tracts have fewer than 100 households with no cars. But some tracts have more than 500 households with no vehicles.
Fig A4: The majoirty of census tracts have fewer than 100 households with no cars. But some tracts have more than 500 households with no vehicles.

Figure 36: Vehicle Ownership Dot Density Map

Vehicle Ownership Dot Density Map

Figure 37: Highest income neighborhoods are north of downtown.

Highest income neighborhoods are north of downtown.

Figure 38: Census tracts by income, population density, and # of adults Living with their parents.

Census tracts by income, population density, and # of adults Living with their parents.